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A useful way to understand Python is to separate the goal, the input, the model or tool, and the way the output is evaluated. One important area is common mistakes. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what...

I wanted to start a broader community experiences thread around Google Image Generation. A useful angle is performance, but it also connects with learning curve. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare...